We describe the design and evaluation of an innovative course for beginning undergraduate mathematics students. The course is delivered almost entirely online, making extensive use of computer-aided assessment to provide students with practice problems. We outline various ideas from education research that informed the design of the course, and describe how these are put into practice. We present quantitative evaluation of the impact on students' subsequent performance (N=1401), as well as qualitative analysis of interviews with a sample of 14 students who took the course. We find evidence that the course has helped to reduce an attainment gap among incoming students, and suggest that the design ideas could be applied more widely to other courses.
This script is used to gather and anonymise data from a separate directory (not included in this respository).
It writes the anonymised data to the data-ANON folder.
This script:
- reads in the anonymised data files
- presents summary statistics (including Table 3 and the histogram from the appendix of the paper)
- assembles the results data into
ANON_student-data.csvready for further analysis. In this data file, each row contains data from a single student, with columns:cohort= academic year in which the student took the Year 1 course(s)anon_id= a randomly-generated string uniquely identifying each studentPreandPost= score out of 100 on the MDT, at the start of Semester 1 and Semester 2 respectivelyILA/CAP/FAC= score out of 100 giving the final grade in each coursetook_FAC= either "FAC" or "No FAC" according to whether the student took FAC
This script carries out the main (Bayesian) statistical analyses, and generates the other figures and tables that appear in the paper.
It relies on the R_rainclouds.R script from https://github.com/RainCloudPlots/RainCloudPlots/blob/master/tutorial_R/R_rainclouds.R to produce the raincloud plots near the start.